Danyu Liu
ABSTRACT
In this paper, we demonstrate JRV, a new data mining visualization tool for the knowledge discovery process where the user and computer can cooperate with each other. First, the computer can be instructed by the user interactively to compute values of several evaluation functions. Then, the user can take advantage of domain knowledge and assess the intermediate results obtained. Furthermore, by providing effective and efficient data visualization, the pattern recognition capacities of users can be greatly improved. Instead of being limited to two attributes at a given time in independence diagrams, this novel tool will allow simultaneous analyses of multiple attribute dependencies using four different drawing panels. Also, by utilizing the existing techniques of data visualization, we design a general model which can handle both categorical and numerical attributes in an intuitive way. With this model, we can identify patterns of interests efficiently. Through actual examples, we show that it might help users to find novel attribute relationships. This work is supported by NIH grant #RO1-CA98932-01.
- Berchtold S., Jagadish H. V., Ross K. A.: "Independence Diagrams: A Technique for Visual Data Mining", Proc. 4th Intl. Conf. on Knowledge Discovery and Data Mining (KDD'98), New York City, 1998, pp. 139--143.Google Scholar
- Coppersmith D., Hong S. J., Hosking J. R. M.: "Partitioning Nominal Attributes in Decision Trees", Data Mining and Knowledge Discovery, an International Journal, Kluwer Academic Publishers, Vol.3, 1999, pp. 197--21 Google ScholarDigital Library
- E. Kandogan. Visualizing Multi-Dimensional Clusters, Trends, and Outliers using Star Coordinates. Proc. ACM SIGKDD '01, pp. 107--116, 2001. Google ScholarDigital Library
- Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39, 11. Google ScholarDigital Library
- J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000. Google ScholarDigital Library
- J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufman, 1993. Google ScholarDigital Library
- L. Breiman et. al. CART, Classification and Regression Trees. Wadsworth, Belmont, 1984.Google Scholar
- M. Ankerst, C. Elsen, M. Ester, and H.-P. Kriegel. Visual classification: An interactive approach to decision tree construction. Proc. 5th Intl. Conf. on Knowledge Discovery and Data Mining (KDD '99), pp. 392--396, 1999. Google ScholarDigital Library
- M.Ankerst, Keim D. A. and Kriegel H.-P.: "Circle Segments: A Technique for Visually Exploring Large MultidimensionalGoogle Scholar
- Data Sets", Proc. Visualization '96, Hot Topic Session, San Francisco, CA, 1996.Google Scholar
- M. Ankerst, M. Ester, and H.-P. Kriegel. Towards an effective cooperation of the user and the computer for classification. Proc. 6th Intl. Conf. on Knowledge Discovery and Data Mining (KDD '00), 2000. Google ScholarDigital Library
- Mehta M., Agrawal R., Rissanen J.: "SLIQ: A Fast Scalable Classifier for Data Mining", Proc. of the Int. Conf. on Extending Database Technology (EDBT '96), Avignon, France, 1996. Google ScholarDigital Library
- Michie D., Spiegelhalter D. J., Taylor C. C.: "Machine Learning, Neural and Statistical Classification", Ellis Horwood, 1994. See also http://www.ncc.up.pt/liacc/ML/statlog/datasets.html. Google ScholarDigital Library
- Paterson, A., and Niblett, T. B. (1982). ACLS Manual. Edinburgh: Intelligent Terminals Ltd.Google Scholar
- Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106. Google ScholarCross Ref
Index Terms
- JRV: an interactive tool for data mining visualization
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